Improving Collision Avoidance Behavior of a Target-Searching Algorithm for Kilobots

Collision avoidance in the area of swarm robotics is very important. The lacking ability of such collision avoidance is mentioned as one important reason for the sparse distribution of the small test robots named Kilobots. In this research paper, two new algorithms providing a collision avoidance strategy are presented and compared with previous research results. The first algorithm uses randomness to decide which one of several approaching Kilobots are stopped for a defined time before starting to move again. The second algorithm tries   to determine the assumed position of approaching Kilobots based on its radio signal strength and then to move away in the opposite direction by rotation. The results, especially of the second algorithm, are promising as the number of collisions can be significantly reduced

A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots

Minimalist robots are functionally highly restricted but well suited for swarm robotic applications because of their low costs and small sizes. Connectivity maintenance and collision avoidance are challenging in minimalist swarm robotic systems due to a short communication range and the lack of positional and directional sensing. In this paper, we introduce a selforganizing control strategy for collective flocking of a swarm of minimalist robots with an aim to improve swarm connectivity and to reduce the chance of collision between robots. Based on the relative positional information built up via collaborations, each robot determines a collision-free operational polygon. This scheduling scheme coordinates the motion of the robots by dividing them into one group of immobile and one group of mobile robots, such that each mobile robot is surrounded by immobile robots serving as beacons. In addition, we introduce a cohesive force into motion planning, which has been shown to play an important role in maintaining a swarm during flocking. A new quantitative metric is introduced for measuring the connectivity of a swarm of agents with local communications, thereby evaluating the performance of the proposed control scheme. We run extensive simulations using simulated Kilobts to examine the influence of different sources of noise and the size of swarms on the connectivity in the swarm and the speed of flocking. Finally, we implement the proposed algorithm on a swarm of real Kilobots to compare the flocking performance with and without the proposed control strategy for coordinated and collective motion

A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots

Shirazi AR, Jin Y. A Strategy for Self-Organized Coordinated Motion of a Swarm of Minimalist Robots. IEEE Transactions on Emerging Topics in Computational Intelligence. 2017;1(5):326-338.Minimalist robots are functionally highly restricted but well suited for swarm robotic applications because of their low costs and small sizes. Connectivity maintenance and collision avoidance are challenging in minimalist swarm robotic systems due to a short communication range and the lack of positional and directional sensing. In this paper, we introduce a self-organizing control strategy for collective flocking of a swarm of minimalist robots with an aim to improve swarm connectivity and to reduce the chance of collision between robots. Based on the relative positional information built up via collaborations, each robot determines a collision-free operational polygon. This scheduling scheme coordinates the motion of the robots by dividing them into one group of immobile and one group of mobile robots, such that each mobile robot is surrounded by immobile robots serving as beacons. In addition, we introduce a cohesive force into motion planning, which has been shown to play an important role in maintaining a swarm during flocking. A new quantitative metric is introduced for measuring the connectivity of a swarm of agents with local communications, thereby, evaluating the performance of the proposed control scheme. We run extensive simulations using simulated Kilobots to examine the influence of different sources of noise and the size of swarms on the connectivity in the swarm and the speed of flocking. Finally, we implement the proposed algorithm on a swarm of real Kilobots to compare the flocking performance with and without the proposed control strategy for coordinated and collective motion